Rotation-covariant visual concept detection using steerable Riesz wavelets and bags of visual words

Distinct texture classes are often sharing several visual concepts. Texture instances from different classes are sharing regions in the feature hyperspace, which results in ill-defined classification configurations. In this work, we detect rotation-covariant visual concepts using steerable Riesz wavelets and bags of visual words. In a first step, K-means clustering is used to detect visual concepts in the hyperspace of the energies of steerable Riesz wavelets. The coordinates of the clusters are used to construct templates from linear combinations of the Riesz components that are corresponding to visual concepts. The visualization of these templates allows verifying the relevance of the concepts modeled. Then, the local orientations of each template are optimized to maximize their response, which is carried out analytically and can still be expressed as a linear combination of the initial steerable Riesz templates. The texture classes are learned in the feature space composed of the concatenation of the maximum responses of each visual concept using support vector machines. An experimental evaluation using the Outex TC 00010 test suite allowed a classification accuracy of 97.5%, which demonstrates the feasibility of the proposed approach. An optimal number K = 20 of clusters is required to model the visual concepts, which was found to be fewer than the number of classes. This shows that higher-level classes are sharing low-level visual concepts. The importance of rotation-covariant visual concept modeling is highlighted by allowing an absolute gain of more than 30% in accuracy. The visual concepts are modeling the local organization of directions at various scales, which is in accordance with the bottom{up visual information processing sequence of the primal sketch in Marr's theory on vision.